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developing machine learning algorithms that dynamically adjust antioxidant infusion based on continuous clinical data could enable adaptive, personalized dosing, while federated learning models could predict patients at high risk for oxidative organ failure, facilitating pre-emptive intervention

These providers maintain independent medical judgment while utilizing Ro's technology infrastructure for service delivery, patient communication, and care coordination

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These findings highlight the potential of ML to uncover metabolic signatures that differentiate early-stage T2DM from advanced stages with complications, thereby enabling risk stratification and targeted interventions (16)